In the last ve years, there has been numerous applications of wavelets and multiresolution analysis in many elds of computer graphics as di erent as geometric modelling, volume visualization or illumination modelling. Classical multiresolution analysis is based on the knowledge of a nested set of functional spaces in which the successive approximations of a given function convergeto that function, and can be e ciently computed. This paper rst proposes a theoretical framework which enables multiresolution analysis even if the functional spaces are not nested, as long as they still have the property that the successiveapproximationsconvergeto the given function. Based on this concept we nally introduce a new multiresolution analysis with exact reconstruction for large data sets de ned on uniform grids. We construct a one-parameter family of multiresolution analyses which is a blending of Haar and linear multiresolution.